CN114880796B - Tolerance analysis method for aircraft assembly process optimization - Google Patents
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Abstract
The invention discloses a tolerance analysis method for aircraft assembly process optimization. The aim of optimizing object determination of an assembler taking multiple factors into consideration is fulfilled by calculating the assembly qualification rate, the process capability index and the deviation source sensitivity, and the purpose of optimizing object determination in a targeted manner is achieved, so that the efficiency and the accuracy of assembly process optimization are improved. The quantitative calculation of the optimization quantity of the optimization object is realized through the tolerance optimization numerical calculation, the complicated process of determining the optimization quantity through multiple simulation is solved, and the optimization efficiency of the assembly process is improved. The aircraft assembly process optimization method based on tolerance analysis simultaneously considers the aircraft assembly requirements and the on-site assembly capability, and reduces the difficulty of on-site assembly under the condition of meeting the assembly requirements.
Description
Technical Field
The invention belongs to the field of aviation manufacturing engineering/aircraft assembly, and relates to a tolerance analysis method for aircraft assembly process optimization.
Background
The optimization of the assembly process is a key means for improving the feasibility of the aircraft assembly process and ensuring the aircraft assembly quality. The main purpose of aircraft assembly process optimization is to ensure that the process capability index is within a reasonable range, and balance between quality and cost is achieved. From the perspective of assembly tolerance analysis, the assembly process optimization mainly comprises two aspects, namely optimizing tolerance values on one hand and optimizing process parameters such as positioning references, positioning methods, assembly sequences and the like in the assembly process on the other hand. How to determine the object of the assembly process optimization and give an optimization scheme according to the result of the assembly tolerance analysis and ensure the balance of the aircraft assembly requirement and the on-site assembly capability is the key point of the assembly process optimization.
The conventional aircraft assembly process optimization aims at different assembly process capacity level grades and assembly target requirements, rough assembly process optimization measures are provided by adjusting assembly processes and parameters for many times according to experience, and an engineer's experience and a large number of loop iterations are needed to determine an optimization object and the optimization quantity of the optimization object. In terms of assembly sequence optimization and planning, an intelligent optimization algorithm library (Jingshi Kai, li Liansheng, zeng Sen) for product assembly sequence planning provides a method for solving the assembly sequence planning problem by using the intelligent optimization algorithm library, wherein the algorithm library mainly comprises an algorithm advisor and an algorithm pool, and the algorithm pool comprises three intelligent optimization algorithms, namely an improved ant colony algorithm, a simulated annealing algorithm, a genetic algorithm and the like. A parallel assembly sequence planning method based on a fuzzy rough set is provided by a parallel assembly sequence planning method (Hu Xiaomei, cinnamomum, tao) based on the fuzzy rough set. In the aspect of assembly path planning and optimization, an aircraft product assembly path planning technology I (Qiu/26068; wei Shengmin, cheng Hui) based on space scanning builds a guide model based on assembly sequence, obtains an assembly path by a space scanning method by taking the guide model as a constraint, optimizes the assembly path by a parameterized analysis method, and improves the efficiency of assembly path planning; the path feedback method (Yu Jianfeng, cheng Hui, yao Ding) for evaluating the assembly sequence of the complex product provides an assembly path feedback method considering the influence of manufacturing resources, solves an assembly process chain model by utilizing a swept volume closure algorithm to obtain an assembly path, evaluates the quality of the assembly sequence by parameterizing the assembly path, and finally generates a reasonable assembly path. In the aspect of assembly process optimization based on quality control, a parallel tolerance fuzzy optimization mathematical model of CAD/CAPP integration is provided by a fuzzy optimization design text (Liu Yusheng, yang will be new, wu Zhao and the like) of tolerance in CAD/CAPP integration, an artificial intelligence-based tolerance optimization method is provided in the research of a tolerance parallel design method in a virtual assembly environment (Ji Shuping), and finally, a genetic algorithm is utilized to carry out repeated iterative solution on a tolerance value. Tolerance design based on statistical tolerance and mass loss-one (Kuang Bing, yellow hair, zhong Yan, for example) proposes a parallel tolerance design approach that considers both tooling costs and mass loss.
The aircraft assembly process optimization has a plurality of influencing factors, the optimization objects and the optimization quantity are difficult to determine, and meanwhile, the assembly design requirements and the field assembly capacity are required to be balanced in the optimization process, so that the following problems still exist in the current aircraft assembly process optimization:
1) The optimization of the assembly process for the problems of out-of-tolerance assembly and the like is not realized. The main works related to the optimization of the assembly process at the present stage are focused on how to shorten the assembly path, optimize the assembly sequence and the like, the assembly process is not optimized with the aim of assembly quality control, and the control of the final assembly precision at the preparation stage of the assembly process cannot be realized.
2) The optimization object determines poor pertinence. The aircraft assembly process is complex, the factors influencing the assembly quality are numerous, the determination of the optimized object at present mainly depends on the experience of engineers and repeated simulation iteration, and the problems of low determination efficiency and poor pertinence of the optimized object exist.
3) And calculating the optimization quantity of the tolerance value. Study of quantitative optimization methods. The existing assembly process and quality optimization are mainly realized by controlling the manufacturing tolerance of the parts, so that the manufacturing cost of the parts is increased. However, the number of assembly tolerances is large and the assembly tolerances are mutually coupled, and the determination of the tolerance optimization quantity lacks quantitative calculation methods, so that the efficiency and quality of the assembly process optimization are affected.
Aiming at the problems, the patent provides an aircraft assembly process optimization method based on tolerance analysis, which can rapidly and pertinently determine an optimization object on the premise of considering assembly requirements and assembly site process capability balance, and can quantitatively calculate tolerance values at the same time, thereby improving the efficiency and quality of aircraft assembly process optimization.
Disclosure of Invention
In order to solve the problems that an aircraft assembly process optimization object is difficult to determine and tolerance optimization quantity cannot be calculated quantitatively, the invention provides an aircraft assembly process optimization method based on tolerance analysis. According to the method, key characteristics are determined by using an aircraft assembly tolerance simulation system environment, aircraft assembly tolerance simulation, assembly qualification rate analysis and process capability calculation, assembly process optimization content judgment, tolerance numerical value optimization and assembly process parameter optimization are carried out, quantitative calculation of assembly tolerance optimization numerical values, rapid determination of an optimization object are realized, aircraft assembly process optimization based on tolerance analysis is carried out, and efficiency and accuracy of aircraft assembly process optimization are improved.
The aim of the invention is achieved by the following technical scheme:
a tolerance analysis method for aircraft assembly process optimization comprises the following steps:
step 1: determining key characteristics; based on aircraft assembly requirements, key characteristics of the assembly requirements are determined.
Step 2: assembling tolerance simulation; and (3) importing the aircraft assembly three-dimensional model into a tolerance simulation software environment, performing tolerance simulation modeling, and performing assembly tolerance simulation on the key characteristics determined in the step (1).
Step 3: the assembly tolerance analysis comprises the calculation of three parameters, namely assembly qualification rate, assembly process capability index and deviation source sensitivity;
and 3, calculating three parameters, namely, assembly qualification rate, assembly process capability index and deviation source sensitivity based on the aircraft assembly tolerance simulation result in the step 2. The assembly process capability index is shown in formula (1).
Wherein:
USL denotes a design given tolerance upper line;
LSL means design given tolerance down line;
sigma represents the overall standard deviation of the functional requirements.
The Cp is adopted to estimate the assembly process capability on the premise that the sample distribution center coincides with the tolerance center, namely the overall average value of the samples is equal to the average value, and the influence of random deviation in the assembly process on the assembly precision is ignored. When random errors of the assembly system are considered, the statistical sample distribution center deviates from the tolerance center, and the evaluation of the assembly process capability by Cp can lead to erroneous results. Therefore, the process capability index Cpk is used at this time, and the calculation is as shown in formula (2).
Wherein:
μ is the overall mean of the functional requirements, and the other parameters are the same as in equation (1).
Wherein the assembling qualification rate is calculated by adopting a Monte Carlo method. The deviation source sensitivity calculation adopts a method for solving deviation of an assembly tolerance simulation model. The calculation method of the two parameters is not included in the invention, and is an essential step for realizing the invention.
Step 4: determining out-of-tolerance key characteristics;
step 5: determining an optimization object;
step 6: optimizing tolerance values based on the CP;
step 7: optimizing parameters of an assembly process;
step 8: and the assembly process scheme is determined.
The step 4 of determining the out-of-tolerance key characteristic is to determine the out-of-tolerance key characteristic according to the calculation result of the step 3.
Step 5 determines that the optimization object takes the out-of-tolerance key characteristic as the assembly process optimization object. The optimization object includes, besides the critical characteristics of the ultra-poor, the critical characteristics of the process capability index of more than 1.0 and less than 1.67, and the critical characteristics of the ultra-process capability index range. In optimizing object determination, the out-of-tolerance key characteristics have a higher priority than the key characteristics of the out-of-tolerance capability index range.
The step 6 is calculated according to a formula (3) based on the tolerance numerical optimization of the CP.
Wherein:
T j representing designing a given jth source tolerance of deviation;
T j ' is an optimized tolerance;
Cp min cp is the lower limit of the process capability index at economic accuracy min ＝1.0；
Cp max Cp is the upper limit of the process capability index at economic accuracy max ＝1.67；
The step 7 of optimizing the assembly process parameters is to optimize the assembly process by taking the assembly sequence, the positioning mode and the like as optimization objects;
and step 8, determining an assembly process scheme when the calculated values of the optimized assembly qualification rate, the process capability index and the like meet the design and manufacturing requirements.
The invention has the beneficial effects that:
the aircraft assembly process optimization method based on tolerance analysis has the following implementation effects: 1) The aim of optimizing object determination of an assembler taking multiple factors into consideration is fulfilled by calculating the assembly qualification rate, the process capability index and the deviation source sensitivity, and the purpose of optimizing object determination in a targeted manner is achieved, so that the efficiency and the accuracy of assembly process optimization are improved. 2) The quantitative calculation of the optimization quantity of the optimization object is realized through the tolerance optimization numerical calculation, the complicated process of determining the optimization quantity through multiple simulation is solved, and the optimization efficiency of the assembly process is improved. 3) The aircraft assembly process optimization method based on tolerance analysis simultaneously considers the aircraft assembly requirements and the on-site assembly capability, and reduces the difficulty of on-site assembly under the condition of meeting the assembly requirements.
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FIG. 1 is an aircraft assembly process optimization flow based on tolerance analysis.
Detailed Description
Embodiments of the present invention will be described in detail below, the nature of the implementations being illustrated in the drawings, wherein like reference numerals refer to like elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
The invention relates to a method for optimizing an aircraft assembly process based on tolerance analysis, which is established on a tool with an aircraft assembly tolerance simulation system and is used for realizing tolerance optimization numerical calculation (shown in figure 1).
The following describes the implementation of the present invention in detail with reference to fig. 1, and this embodiment is implemented on the premise of the technical scheme of the present invention, and provides a detailed implementation manner and a specific implementation process of the present invention, but the protection scope of the present invention is not limited to the following entity Shi Shili.
Step 1: key characteristics are determined. According to design requirements, key characteristics, including key product characteristics and key control characteristics, are determined by combining an assembly process and are used as targets and optimized objects of assembly precision analysis.
Step 2: and performing assembly tolerance simulation analysis. And according to the determined key characteristics, performing assembly tolerance simulation by adopting a Monte Carlo method based on an assembly tolerance simulation model in an aircraft assembly tolerance simulation system environment to obtain statistical parameters such as assembly qualification rate of the key characteristics.
Step 3: assembly process capability and bias source sensitivity are calculated. And (3) calculating the process capability index according to the formula (1) or (2) according to the assembly tolerance simulation result of the step 2.
Step 4: and determining out-of-tolerance key characteristics. Judging whether the assembly precision of the key characteristics is out of tolerance according to the process capability index, if the assembly precision meets the design requirement, and Cp is more than 1.0 and less than or equal to 1.67, the key characteristics are not out of tolerance, then outputting an assembly process scheme, and finishing the optimization of the assembly process. Otherwise, jumping to the step 5 to continue the assembly process optimization.
Step 5: an optimization object is determined. The key characteristics that the assembly precision does not meet the design requirement or Cp is more than 1.0 and less than or equal to 1.67 are all required to be used as optimization objects. Wherein, the key characteristic that the assembly precision does not meet the design requirement is taken as a primary and necessary optimization object, and the key characteristic that Cp is more than 1.0 and less than or equal to 1.67 is taken as a secondary priority optimization object.
Step 6: and determining the assembly process optimization content of the optimization object. Based on the optimization object determined in the step 5, whether tolerance values of the optimization key control characteristics or relevant assembly process parameters are further judged. Judging whether the sensitivity of the deviation source of the optimization object is far greater than 1, if so, indicating that the deviation of the deviation source is rapidly amplified in the deviation transmission, and optimizing the tolerance value of the deviation source basically has no influence on the assembly precision of key characteristics, so that the assembly process parameters such as the assembly sequence, the positioning scheme or the connection process are selected.
And 7, calculating tolerance optimization numerical values. The tolerance optimization value is calculated according to a formula (3);
and 8, re-simulating the assembly tolerance after optimization. Substituting the optimized parameters into an assembly tolerance simulation model, jumping to the step 2, carrying out re-simulation of the assembly tolerance after optimization, and analyzing whether the simulation value of the assembly key characteristic meets the design requirement and the manufacturing process capability. If yes, forming an assembly process optimization scheme, and ending the flow. If the critical characteristics are still out of tolerance, the process jumps to step 6.
Claims (4)
1. An aircraft assembly process optimization method based on tolerance analysis is characterized by comprising the following steps:
step 1: determining key characteristics; determining key characteristics of the assembly requirements based on the aircraft assembly requirements;
step 2: assembling tolerance simulation; introducing the aircraft assembly three-dimensional model into a tolerance simulation software environment, performing tolerance simulation modeling, and performing assembly tolerance simulation on the key characteristics determined in the step 1;
step 3: the assembly tolerance analysis comprises the calculation of three parameters, namely assembly qualification rate, assembly process capability index and deviation source sensitivity;
step 3 is based on the aircraft assembly tolerance simulation result of step 2, calculating three parameters of assembly qualification rate, assembly process capability index and deviation source sensitivity; the assembly process capability index is shown in formula (1);
wherein:
USL denotes a design given tolerance upper line;
LSL means design given tolerance down line;
sigma represents the overall standard deviation of the functional requirements;
the process capability index Cpk is adopted, and the calculation is shown in a formula (2);
wherein:
μ is the overall mean of the functional requirements, and other parameters are the same as formula (1);
step 4: determining out-of-tolerance key characteristics;
step 5: determining an optimization object;
step 6: optimizing tolerance values based on the CP;
the step 6 is calculated according to a formula (3) based on the tolerance numerical optimization of the CP;
wherein:
T j representing designing a given jth source tolerance of deviation;
T j ' is an optimized tolerance;
Cp min is the lower limit of the process capability index under the economic precision;
Cp max is the upper limit of the process capability index under the economic precision;
step 7: optimizing parameters of an assembly process;
the step 7 of optimizing the assembly process parameters is to optimize the assembly process by taking the assembly sequence and the positioning mode as an optimization object;
step 8: determining an assembly process scheme;
and step 8, determining an assembly process scheme after the optimized assembly qualification rate and the process capability index calculated value meet the design and manufacturing requirements.
2. An aircraft assembly process optimization method based on tolerance analysis as claimed in claim 1, wherein said step 5 determines the optimization object specifically as follows: taking the out-of-tolerance key characteristics as an assembly process optimization object; the optimization object comprises critical characteristics of process capability indexes larger than 1.0 and smaller than 1.67 and critical characteristics of a super process capability index range besides the critical characteristics of super difference; in optimizing object determination, the out-of-tolerance key characteristics have a higher priority than the key characteristics of the out-of-tolerance capability index range.
3. An aircraft assembly process optimization method based on tolerance analysis as claimed in claim 1, wherein in said step 3, the assembly qualification rate is calculated by using a monte carlo method.
4. An aircraft assembly process optimization method based on tolerance analysis as claimed in claim 1, wherein in said step 3, deviation source sensitivity calculation adopts a method of deriving deviation from an assembly tolerance simulation model.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103164584A (en) * | 2013-03-29 | 2013-06-19 | 江西洪都航空工业集团有限责任公司 | Calculation method of coordination accuracy based on key characteristics |
| CN108629114A (en) * | 2018-05-04 | 2018-10-09 | 西北工业大学 | A kind of fabrication tolerance simulating analysis towards the deformation of aircraft assembly connection |
| CN113779885A (en) * | 2021-09-16 | 2021-12-10 | 南京航空航天大学 | Tolerance optimization method based on genetic algorithm |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103164584A (en) * | 2013-03-29 | 2013-06-19 | 江西洪都航空工业集团有限责任公司 | Calculation method of coordination accuracy based on key characteristics |
| CN108629114A (en) * | 2018-05-04 | 2018-10-09 | 西北工业大学 | A kind of fabrication tolerance simulating analysis towards the deformation of aircraft assembly connection |
| CN113779885A (en) * | 2021-09-16 | 2021-12-10 | 南京航空航天大学 | Tolerance optimization method based on genetic algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 基于关键特性的飞机装配公差分析方法;赵东平;张辉;郭津呈;;西安航空学院学报;20200120(第01期);全文 * |
| 外翼翼盒对接面装配协调的容差分析及工艺优化;汪玮;匡翠;;机械设计与制造;20141208(第12期);全文 * |
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